There are 4 steps to the calibration:
- gleak
- Erev
- txmt
- tau_lpf
- Arrange the board's xml file so the chip and synapse numbers you are looking to calibrate are first in the file
- Match the chip and synapse numbers in syn_f_vs_g_sleak.py (f_vs_erev_sleak.py) to the desired chip and synapse numbers AND BE SURE TO SAVE.
- In spring, run syn_f_vs_g_sleak.py (f_vs_erev_sleak.py)
- Set the chip and synapse numbers in calib_syn_f_vs_g_sleak.py (calib_f_vs_erev_sleak.py) to the desired chip and synapse numbers AND BE SURE TO SAVE.
- Run calib_syn_f_vs_g_sleak.py (calib_f_vs_erev_sleak.py) in ipython --pylab
- Execute extract_syn_param_g_lksoma.py (extract_syn_param_erev_lksoma.py) in ipython --pylab, and then call run_default with the appropriate bif file, board name, chip number, and synapse number.
- Use median values in histogram as parameters in xml file
- In neuro-boa/apps/calibrate_neuron/calibrate_synapse/calibrate_pe/calibrate_pe.cpp (calibrate_vleakpf/calibrate_vleakpf.cpp):
- make sure the appropriate chip calibration is pushed back
- CALIBRATION_DAC_FILE.push_back(<check this>);
- CALIBRATION_ADC_FILE.push_back(<check this>);
- make sure the appropriate chip number is pushed back
- selected_chips.push_back(<chip number>);
- select the appropriate chip in the for loop around ~line 621
- run make
- run calibrate_pe (calibrate_vleakpf)
- For txmt:
- change pw_values.csv to pw_values_<board>_<chip>.csv
- In fit_2d_1coeff.py
- change filename to match data
- select synapse
- run fit_2d_1coeff.py
- set C1 to median value
- set C3 to 0
- For tau_lpf:
- change the data/ folder name to data<chip num>/
- In fit_tau.py
- change data folder to match chip number
- run fit_tau.py
- set tau_lpf to median value
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